Elad Inbar - 205358211 Ido Livne - 203834734 Chai Hadad - 209268135

Import¶

In [ ]:
import csv
from torch.nn.utils.rnn import pad_sequence
import gensim
from gensim.models import Word2Vec
import torch
from torch import nn 
from torch.utils.data import Dataset, DataLoader
import torchtext
import os
import random
import pprint as pp
import math
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import plotly.express as px
import plotly.graph_objects as go
import plotly.offline as pyo
from plotly.offline import init_notebook_mode, iplot
import seaborn as sns
import numpy as np
import sklearn
from sklearn import tree
from sklearn import impute
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics import mean_absolute_error
from sklearn import ensemble 
import itertools
from torchtext.data import get_tokenizer
import time
import xgboost as xgb
import lightgbm as lgb
from sklearn.metrics import mean_squared_error

Initialize Runtime¶

In [2]:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
is_colab_mode = False
device
Out[2]:
device(type='cuda')

Google Colab Init¶

In [3]:
# !pip uninstall torchtext
In [4]:
# !pip install torchtext==0.8.1
In [5]:
# is_colab_mode = True
In [6]:
# import nltk
# from nltk.tokenize import sent_tokenize, word_tokenize
# !pip install -q kaggle
In [7]:
# !mkdir ~/.kaggle
In [8]:
# # Google Colab

# from google.colab import drive
# drive.mount('/content/drive')
In [9]:
# # Google Colab
# !cp -r "/content/drive/My Drive/Deep Learning/kaggle.json" "/content"
In [10]:
# !cp kaggle.json ~/.kaggle/
In [11]:
# !chmod 600 ~/.kaggle/kaggle.json
In [12]:
# !kaggle competitions download -c home-depot-product-search-relevance
In [13]:
# # Google Colab
# !cp -r "/content/drive/My Drive/Deep Learning/Assignment 4/solution.csv" "/content/solution.csv"
In [14]:
# !unzip /content/home-depot-product-search-relevance.zip
In [15]:
# Unnecessary

# !unzip /content/attributes.csv.zip
# !unzip /content/product_descriptions.csv.zip
# !unzip /content/sample_submission.csv.zip
# !unzip /content/test.csv.zip
# !unzip /content/train.csv.zip
In [16]:
# nltk.download('punkt')

Define dataset paths¶

In [30]:
train_path = '/content/train.csv' if is_colab_mode else 'content/train.csv'
test_path = '/content/test.csv' if is_colab_mode else 'content/test.csv'
test_labels_path = '/content/solution.csv' if is_colab_mode else 'content/solution.csv'
mod_train_path = 'content/train_mod.csv'
mod_test_path = 'content/test_mod.csv'
descriptions_path = 'content/product_descriptions.csv'

Exploratory Data Analysis¶

In [6]:
if is_colab_mode:
    # train_df = pd.read_csv(train_path + ".zip", encoding="latin-1", compression='zip')
    train_df = pd.read_csv(train_path + ".zip", encoding="ISO-8859-1", compression='zip')

else:
    train_df = pd.read_csv(train_path, encoding="ISO-8859-1")
train_df
Out[6]:
id product_uid product_title search_term relevance
0 2 100001 Simpson Strong-Tie 12-Gauge Angle angle bracket 3.00
1 3 100001 Simpson Strong-Tie 12-Gauge Angle l bracket 2.50
2 9 100002 BEHR Premium Textured DeckOver 1-gal. #SC-141 ... deck over 3.00
3 16 100005 Delta Vero 1-Handle Shower Only Faucet Trim Ki... rain shower head 2.33
4 17 100005 Delta Vero 1-Handle Shower Only Faucet Trim Ki... shower only faucet 2.67
... ... ... ... ... ...
74062 221457 206638 Atlantic Windowpane 576 CD or 192 DVD Blu-Ray ... tv riser glass 1.00
74063 221458 206639 Philips 40-Watt Halogen R20 Flood Light Bulb (... r20 halogen light 3.00
74064 221463 206641 Schlage Camelot In-Active Aged Bronze Handlese... schlage lock siena half dummy knob with 2.33
74065 221471 206648 Plastec 11 in. x 24 in. Rose Garden Wall Decor... zen garden decor 3.00
74066 221473 206650 LICHTENBERG Pool Blue No. 918 Millennial Ryan ... fine sheer curtain 63 inches 2.33

74067 rows × 5 columns

In [7]:
if is_colab_mode:
    test_df =  pd.read_csv(test_path + ".zip", encoding="ISO-8859-1", compression="zip")
else:
    test_df = pd.read_csv(test_path, encoding="ISO-8859-1")
test_df
Out[7]:
id product_uid product_title search_term
0 1 100001 Simpson Strong-Tie 12-Gauge Angle 90 degree bracket
1 4 100001 Simpson Strong-Tie 12-Gauge Angle metal l brackets
2 5 100001 Simpson Strong-Tie 12-Gauge Angle simpson sku able
3 6 100001 Simpson Strong-Tie 12-Gauge Angle simpson strong ties
4 7 100001 Simpson Strong-Tie 12-Gauge Angle simpson strong tie hcc668
... ... ... ... ...
166688 240756 224424 stufurhome Norma 24 in. W x 16 in. D x 34 in. ... 24 whtie storage cabinet
166689 240757 224425 Home Decorators Collection 49 in. D Alessandro... adirondeck cusion
166690 240758 224426 Simpson Strong-Tie HB 3-1/2 x 14 in. Top Flang... hb
166691 240759 224427 1/4 in. -20 tpi x 1-1/2 in. Stainless Steel Bu... hex sockets
166692 240760 224428 Bosch 4 in. Bi-Metal Hole Saw 4 inch hole saw

166693 rows × 4 columns

Plots Functions¶

In [8]:
def plot_relevance_score_distribution(df, df_name):
    sns.displot(df['relevance'], kde=False, bins=20)
    plt.xlabel('Relevance Score')
    plt.ylabel('Count')
    plt.title(f'{df_name} Relevance Score Distribution')
    plt.show()
In [9]:
def plot_word_length_distribution(df, df_name, column, x_title):
    df[column + '_length'] = df[column].apply(lambda x: len(x.split()))
    fig = px.histogram(df, x=column+"_length")
    fig.update_layout(
        title=dict(text=f'{df_name} - {x_title} By Word Length Distribution', font=dict(size=20)),
        xaxis_title=x_title+" Length",
        yaxis_title="Count",
    )
    fig.show()

def plot_characters_length_distribution(df, df_name, column, x_title):
    df[column + '_length'] = df[column].apply(lambda x: len(x))
    fig = px.histogram(df, x=column + "_length")
    fig.update_layout(
        title=dict(text=f'{df_name} - {x_title} By Character Length Distribution', font=dict(size=20)),
        xaxis_title=x_title + " Length",
        yaxis_title="Count",
    )
    fig.show()
In [10]:
def calculate_product_frequency(df, col):
    freq = df.groupby('product_uid').size().reset_index(name='freq')
    # Calculate the mean, min, and max frequency of each product UID
    mean_freq = freq['freq'].mean()
    min_freq = freq['freq'].min()
    max_freq = freq['freq'].max()
    return {'Mean': mean_freq, 'Min': min_freq, 'Max': max_freq}
In [11]:
def plot_loss(epochs, df, title , use_val = True ):

    # create the first trace (scatter plot)
    trace1 = go.Scatter(x=epochs, y=df['train_loss'], mode='lines',  name='train loss')
    if use_val:

        # create the second trace (line plot)
        trace2 = go.Scatter(x=epochs, y=df['val_loss'], mode='lines',  name='validation loss')

        # create the plotly figure object
        fig = go.Figure(data=[trace1, trace2])
    else:
        fig = go.Figure(data=[trace1, trace2])

    # set the plot title and axis labels
    fig.update_layout(
        title=title,
        xaxis_title='Epochs',
        yaxis_title='Loss Value'
    )

    # display the plot
    fig.show()

Analysis Plots¶

Train Analysis¶

In [12]:
freq = calculate_product_frequency(train_df, 'product_uid')
print('Product UID frequency:')
print('Mean: {}'.format(freq['Mean']))
print('Min: {}'.format(freq['Min']))
print('Max: {}'.format(freq['Max']))

Product UID frequency:
Mean: 1.3548758849031408
Min: 1
Max: 21
In [13]:
plot_relevance_score_distribution(train_df, 'Train')
In [14]:
plot_word_length_distribution(train_df, 'Train', 'product_title', "Product Title")
In [15]:
plot_word_length_distribution(train_df, 'Train', 'search_term', "Search Term")
In [16]:
plot_characters_length_distribution(train_df, 'Train', 'product_title', "Product Title")
In [36]:
plot_characters_length_distribution(train_df, 'Train', 'search_term', "Search Term")

Test Analysis¶

In [18]:
freq = calculate_product_frequency(test_df, 'product_uid')
print('Product UID frequency:')
print('Mean: {}'.format(freq['Mean']))
print('Min: {}'.format(freq['Min']))
print('Max: {}'.format(freq['Max']))

Product UID frequency:
Mean: 1.7103734865585882
Min: 1
Max: 52
In [20]:
plot_word_length_distribution(test_df, 'Test', 'product_title', "Product Title")
In [22]:
plot_characters_length_distribution(test_df, 'Test', 'product_title', "Product Title")
In [21]:
plot_word_length_distribution(test_df, 'Test', 'search_term', "Search Term")
In [23]:
plot_characters_length_distribution(test_df, 'Test', 'search_term', "Search Term")

product description analysis¶

In [31]:
product_descriptions_df = pd.read_csv(descriptions_path + ".zip", encoding="ISO-8859-1", compression='zip')
product_descriptions_df.head()
Out[31]:
product_uid product_description
0 100001 Not only do angles make joints stronger, they ...
1 100002 BEHR Premium Textured DECKOVER is an innovativ...
2 100003 Classic architecture meets contemporary design...
3 100004 The Grape Solar 265-Watt Polycrystalline PV So...
4 100005 Update your bathroom with the Delta Vero Singl...
In [32]:
plot_word_length_distribution(product_descriptions_df, 'Product Description Data Frame', 'product_description', 'Product Description')
In [33]:
plot_characters_length_distribution(product_descriptions_df, 'Product Description Data Frame', 'product_description', 'Product Description')

Pre-Processing¶

General Pre-Processing¶

Remove all 'ignored' test samples

In [27]:
solution_df = pd.read_csv(test_labels_path, encoding="ISO-8859-1")
In [28]:
mod_test_df = pd.merge(test_df, solution_df, on='id')
mod_test_df = mod_test_df[mod_test_df['relevance'] != -1]
mod_test_df.drop(columns=['Usage'], axis=1, inplace=True)
mod_test_df.reset_index(drop=True, inplace=True)
mod_test_df
Out[28]:
id product_uid product_title search_term relevance
0 4 100001 Simpson Strong-Tie 12-Gauge Angle metal l brackets 2.33
1 5 100001 Simpson Strong-Tie 12-Gauge Angle simpson sku able 2.33
2 6 100001 Simpson Strong-Tie 12-Gauge Angle simpson strong ties 2.67
3 7 100001 Simpson Strong-Tie 12-Gauge Angle simpson strong tie hcc668 2.00
4 10 100003 STERLING Ensemble 33-1/4 in. x 60 in. x 75-1/4... bath and shower kit 2.67
... ... ... ... ... ...
112062 221467 206644 MAX 0-Gauge Super Roofer roof nailing gun 2.33
112063 221468 206645 GROHE Ladylux 3 Pro Single-Handle Pull-Down Du... kitchen pro grout 1.00
112064 221469 206646 60 in. x 30 in. TileKit Bathtub Wall Set in Bo... tilekit 30 x 60 wall bone 2.67
112065 221470 206647 Forum Novelties Girls Little Pink Princess Cos... light switchvers for little girls 1.00
112066 221472 206649 Central Garden and Pet 11.5 in. Silver Grey St... garden pots colared 2.33

112067 rows × 5 columns

removing Upper case letters

In [29]:
mod_test_df['product_title'] = mod_test_df['product_title'].str.lower()
mod_test_df['search_term'] = mod_test_df['search_term'].str.lower()
train_df['product_title'] = mod_test_df['product_title'].str.lower()
train_df['search_term'] = mod_test_df['search_term'].str.lower()

removing conjunction-like words

In [ ]:
def remove_conjuction_words(df, column_name):
    res_df = df.copy()
    conjunction_set = set(['is', 'in' ,'and', 'for', 'or','yet','so','nor','both','whether','but','also','either','neither','just','the','as','then',
                       'than','such','that','after','although','much','by','inasmuch','even','when','where','since','because','whose','before','until','how','like', 'i',
                  'want', 'a', 'an'])
    # found = False
    count = 0
    for i in range(len(res_df.index)):
        for word in conjunction_set:
            df_word = res_df[column_name].iloc[i]
            if ( word  + ' ') in  df_word:
                count += 1
                # print(word)
                # print(f'before: {df_word}')
                res = ' '.join([w for w in df_word.split() if w not in conjunction_set])
                res_df[column_name].iloc[i] = res
                # print(f'after {res_df[column_name].iloc[i]}')
                # print(i)
        #         found = True
        #         break
        # if found:
        #     break
    print(f' changed {count} rows')
    return res_df

train_df = remove_conjuction_words(train_df,'search_term')
train_df = remove_conjuction_words(train_df,'product_title')
mod_test_df = remove_conjuction_words(mod_test_df,'product_title')
mod_test_df = remove_conjuction_words(mod_test_df,'search_term')
In [31]:
train_df.head()
Out[31]:
id product_uid product_title search_term relevance product_title_length search_term_length
0 2 100001 simpson strong-tie 12-gauge angle metal l brackets 3.00 33 13
1 3 100001 simpson strong-tie 12-gauge angle simpson sku able 2.50 33 9
2 9 100002 simpson strong-tie 12-gauge angle simpson strong ties 3.00 79 9
3 16 100005 simpson strong-tie 12-gauge angle simpson strong tie hcc668 2.33 78 16
4 17 100005 sterling ensemble 33-1/4 in. x 60 in. x 75-1/4... bath shower kit 2.67 78 18
In [35]:
plot_relevance_score_distribution(test_df, 'Test')

Analyzing brands and material test and train distribution

In [32]:
atributes_df = pd.read_csv('content/attributes.csv', encoding="ISO-8859-1")
brands_df = atributes_df[atributes_df.name == "MFG Brand Name"]
brands_df['value'] = brands_df['value'].str.lower()
brands_set = set(brands_df.value)

materials_df = atributes_df[atributes_df.name == "Material"]
materials_df['value'] = materials_df['value'].str.lower()
materials_set = set(materials_df.value)

test_uids = set(mod_test_df.product_uid)
train_uids = set(train_df.product_uid) 
# cheking difference between test and train brands
test_only = set()
train_only = set()
both = set()
none = set()
for brand in brands_set:
    in_test = False
    in_train = False
    products_uids = brands_df[brands_df['value'] == brand].product_uid
    for uid in products_uids:
        uid = int(uid)
        if uid in train_uids:
            in_train = True
        if uid in test_uids:
            in_test = True
        if in_test and in_train:
            continue 
    if in_test and in_train:
        both.add(brand)
    elif in_test and not in_train:
        test_only.add(brand)
    elif not in_test and in_train:
        train_only.add(brand)
    else:
        none.add(brand)    
        
print('Brand Distrib')
print(f'difference: test:{len(test_only)}, train: {len(train_only)}')
print(f'in none:{len(none)}')
print(f'in both:{len(both)}')


# cheking difference between test and train materials
test_only = set()
train_only = set()
for material in materials_set:
    in_test = False
    in_train = False
    products_uids = materials_df[materials_df['value'] == material].product_uid
    for uid in products_uids:
        uid = int(uid)
        if uid in train_uids:
            in_train = True
        if uid in test_uids:
            in_test = True
        if in_test and in_train:
            continue 
    if in_test and in_train:
        both.add(material)
    elif in_test and not in_train:
        test_only.add(material)
    elif not in_test and in_train:
        train_only.add(material)
    else:
        none.add(material)
print()
print('Metrial Distrib')
print(f'difference: test:{len(test_only)}, train: {len(train_only)}')
print(f'in none:{len(none)}')
print(f'in both:{len(both)}')

/home/idoliv/.conda/envs/my_env/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/idoliv/.conda/envs/my_env/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


Brand Distrib
difference: test:733, train: 365
in none:412
in both:2765

Metrial Distrib
difference: test:173, train: 106
in none:512
in both:3094

Write the modified dataframes to a .csv file

In [33]:
train_df.to_csv(mod_train_path, index=False)
mod_test_df.to_csv(mod_test_path, index=False)
In [34]:
train_df = pd.read_csv(mod_train_path, encoding="ISO-8859-1")
test_df = pd.read_csv(mod_test_path, encoding="ISO-8859-1")
corpus =  pd.concat([train_df, test_df], axis=0)
In [35]:
train_df.head()
Out[35]:
id product_uid product_title search_term relevance product_title_length search_term_length
0 2 100001 simpson strong-tie 12-gauge angle metal l brackets 3.00 33 13
1 3 100001 simpson strong-tie 12-gauge angle simpson sku able 2.50 33 9
2 9 100002 simpson strong-tie 12-gauge angle simpson strong ties 3.00 79 9
3 16 100005 simpson strong-tie 12-gauge angle simpson strong tie hcc668 2.33 78 16
4 17 100005 sterling ensemble 33-1/4 in. x 60 in. x 75-1/4... bath shower kit 2.67 78 18

Train Validation split¶

beucase out model has a big data, we decided to go with the train - test split validation technique

In [36]:
split_index = math.floor(0.8 * len(train_df.index))
all_train_df = train_df.copy()
val_df = train_df.iloc[split_index:]
val_df.reset_index(drop=True, inplace=True)
train_df = train_df.iloc[:split_index]
train_df.reset_index(drop=True, inplace=True)

Common Functions¶

In [37]:
def RMSE(y_actual, y_predicted):
    MSE = np.square(np.subtract(y_actual,y_predicted)).mean() 
    RMSE = math.sqrt(MSE)
    return RMSE
In [38]:
def MAE(y_actual, y_predicted):
    return mean_absolute_error(y_actual, y_predicted)
In [39]:
def to_tokens(text):
    text = text.replace(' ', ',')
    # text = text.replace('.', ',')
    text = text.replace('\n', ',')
    text = text.replace(';', ',')
    text = text.replace('_', ',')
    text = text.replace('+', ',')
    text = text.replace('-', ',')
    text = text.replace('\'', ',')
    return text.split(',')
In [40]:
def split_text(old_df):
    df = old_df.copy()
    df.search_term = [to_tokens(term) for term in df.search_term]
    df.product_title = [to_tokens(term) for term in df.product_title]
    return df
In [41]:
def gather_feature_extraction_samples(data_loader, model):
    preds_1 = []
    preds_2 = []
    labels = []
    last_print = 0
    for batch, (product_title, search_term, y) in enumerate(data_loader):
        product_title = product_title.to(device)
        search_term = search_term.to(device)
        y = y.to(dtype=torch.float)
        y = y.to(device)
        x1, x2 = model(product_title, search_term)
        labels.append(y.cpu().detach().numpy())
        preds_1.append(x1.cpu().detach().numpy())
        preds_2.append(x2.cpu().detach().numpy())
        if last_print + 0.2 < batch/len(data_loader):
            last_print += 0.2
            print(f'finished {int(100*(batch/len(data_loader)))}%')
    
    preds_1 = np.array(preds_1).squeeze()
    preds_2 = np.array(preds_2).squeeze()
    features = np.concatenate([preds_1, preds_2], axis=1).squeeze()
    print(f'finished extracting')
    return features, labels

LSTM Construction¶

In [42]:
class SiameseNetworkLSTM(nn.Module):
    def __init__(self, embedding_dim, lstm_hidden_dim,dropout, num_layers=1 ):
        super().__init__()
        self.lstm = nn.LSTM(embedding_dim, lstm_hidden_dim,num_layers = num_layers,dropout=dropout, batch_first=True)

    def forward(self,x1,x2):
        output_x1, (hidden_x1, cn_x1) = self.lstm(x1)
        output_x2, (hidden_x2, cn_x2) = self.lstm(x2)
        return hidden_x1,hidden_x2
In [43]:
def normalize_relevance(relevance):
    return (relevance -1 )/2
In [44]:
def train_loop(data_loader, model, device, loss_fn, distance_func):
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
    size = len(data_loader.dataset)
    num_batches = len(data_loader)
    train_loss=0
    counter = 0
    last_print = 0
    for batch, (product_title,search_term,y) in enumerate(data_loader):
        product_title = product_title.to(device)
        search_term = search_term.to(device)
        y = y.to(dtype=torch.float)
        y = y.to(device)
        x1,x2 = model(product_title, search_term)
        pred = distance_func(x1[0],x2[0])
        # pred = (pred *2 )+1
        loss = loss_fn(pred, normalize_relevance(y))
        if type(loss_fn) == type(nn.MSELoss()):
            loss = torch.sqrt(loss)
        train_loss += loss
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if last_print + 0.4 < batch/num_batches:
            last_print += 0.4
            print(f'finished {int(100*(batch/len(data_loader)))}%')
    loss = loss.item()
    train_loss /= num_batches
    print(f'train loss = {train_loss:8f}')
    return train_loss

def test_loop(data_loader, model, device, loss_fn, distance_func, test_text='validation'):
    model.eval()
    cos = nn.CosineSimilarity(dim=1)
    size=len(data_loader.dataset)
    num_batches = len(data_loader)
    test_loss=0
    last_print = 0
    with torch.no_grad():
        for batch, (product_title,search_term,y) in enumerate(data_loader):
            product_title = product_title.to(device)
            search_term = search_term.to(device)
            y = y.to(dtype=torch.float)
            y = y.to(device)
            x1,x2 = model(product_title, search_term)
            pred = distance_func(x1[0],x2[0])
            # pred = (pred *2 )+1
            loss = loss_fn(pred,  normalize_relevance(y))
            if type(loss_fn) == type(nn.MSELoss()):
                loss = torch.sqrt(loss)
            test_loss += loss
            if last_print + 0.4 < batch/num_batches:
                last_print += 0.4
                print(f'finished {int(100*(batch/len(data_loader)))}%')
 
    test_loss /= num_batches
    print(f'{test_text} loss = {test_loss:8f}')
    return test_loss

Character Level Task¶

Naive Solution¶

Count Vectorizer¶

corpus construction

In [45]:
corpus_search = list(train_df['search_term'])
corpus_title = list(train_df['product_title'])
labels =  list(train_df['relevance'])
In [46]:
%%time
title_vectorizer = CountVectorizer(analyzer = 'char')
X_title = title_vectorizer.fit_transform(corpus_title)

search_vectorizer = CountVectorizer(analyzer = 'char',vocabulary= title_vectorizer.get_feature_names_out() )
X_search = search_vectorizer.fit_transform(corpus_search)

preds = list()

for i in range(len(train_df.index)):
    similarity = cosine_similarity(X_search[i], X_title[i])
    similarity = (similarity * 2) +1
    preds.append(similarity[0][0])

CPU times: user 48.5 s, sys: 50.7 ms, total: 48.5 s
Wall time: 48.5 s
In [47]:
df_cosine = pd.DataFrame({'pred': preds, 'label': labels})
df_cosine
Out[47]:
pred label
0 2.191499 3.00
1 2.423877 2.50
2 2.616525 3.00
3 2.478687 2.33
4 2.570552 2.67
... ... ...
59248 2.286515 2.33
59249 2.597965 2.33
59250 1.871454 3.00
59251 2.490930 2.00
59252 2.531216 1.67

59253 rows × 2 columns

now we would like check whether the naive solution is better than ranodmize. we will take the average score and comapre the MSE of average score with the MSE of the naive solution

In [48]:
average_relevance = sum(train_df.relevance) / len(train_df.index)
naive_dist = 0
rand_dist = 0
for idx,label in enumerate(df_cosine.label):
    pred = (df_cosine.pred[idx] /2 ) -1
    naive_dist += abs(df_cosine.pred[idx] - label)
    rand_dist += abs(average_relevance - label)
# print(f'naive distance average {naive_dist/  len(train_df.index)}')

naive distance average 0.4750244491608411
In [49]:
print(f'naive RMSE: {RMSE(list(df_cosine.label),list(df_cosine.pred))}')
average = [average_relevance] * len(train_df.index)

naive RMSE: 0.5921473911533216
In [50]:
print(f'naive MAE: {MAE(list(df_cosine.label),list(df_cosine.pred))}')

naive MAE: 0.47502444916084713

Character Level LSTM Solution¶

configurations¶

In [335]:
batch_size = 32
learning_rate = 1e-3
window_size = 50
embedding_dim = 3 
lstm_hidden_dim = 20 
dropout = 0
num_layers = 1

Embedding¶

In [336]:
%%time
vector_size =3
corpus_splited = split_text(corpus) 
data_all = list(corpus_splited.product_title) + list(corpus_splited.search_term)
data = [list(sentence) for sentence in data_all]
average_word_size = 0
word_count = 0
for sentence in train_df.product_title:
    words = sentence.split()
    for word in words:
        average_word_size += len(word)
        word_count += 1
average_word_size /= word_count
# data
char_embedding = gensim.models.Word2Vec(data, min_count = 1, vector_size =embedding_dim, window = math.floor(average_word_size),sg=1)

CPU times: user 38.1 s, sys: 63.9 ms, total: 38.2 s
Wall time: 14.9 s
In [338]:
print(f'max chars length: {max_len}')

max chars length: 127
In [339]:
char_embedding.wv.similarity('a','b')
Out[339]:
0.7335358

Dataset¶

In [239]:
def embed_sentence(sentence, embedding_model):
    chars = list(sentence)
    return [embedding_model.wv[char] for char in chars]

def pad_sentence(sentence, expected_len):
    if len(sentence) >  expected_len:
        return sentence[:expected_len]
    else:
        pad_len = expected_len -  len(sentence)
        return sentence + [0]*pad_len
In [240]:
class char_dataset(Dataset):
    def __init__(self, data, embedding_model, window_size):
        self.data = data
        self.embedding_model = embedding_model
        self.window_size = window_size
        
    def __len__(self):
        return len(self.data.index)
    
    def __getitem__(self,idx):
        label = self.data.relevance[idx]
        product_title =  list(self.data.product_title[idx])
        product_title = pad_sentence(product_title,self.window_size)
        product_title_embedded = embed_sentence(product_title, self.embedding_model)
        search_term =  list(self.data.search_term[idx])
        search_term = pad_sentence(search_term,self.window_size)
        search_term_embedded = embed_sentence(search_term, self.embedding_model)
        
        return torch.FloatTensor(product_title_embedded) ,torch.FloatTensor(search_term_embedded), label
char_ds_train = char_dataset(split_text(train_df),char_embedding, window_size)
char_ds_val = char_dataset(split_text(val_df),char_embedding, window_size)
char_ds_test = char_dataset(split_text(test_df),char_embedding, window_size)
In [241]:
train_loader = DataLoader(char_ds_train, batch_size=batch_size, shuffle = False)
val_loader = DataLoader(char_ds_val, batch_size=batch_size, shuffle = False)

RMSE¶

for RMSE we used torch mse with torch.sqrt (inside the train loop itself)

Training¶

In [242]:
loss_fn = nn.MSELoss()
char_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout )
In [243]:
%%time
epochs = 10
char_history = []
char_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, char_model, device, loss_fn, nn.CosineSimilarity(dim=1))
    val_loss = test_loop(val_loader, char_model, device, loss_fn,  nn.CosineSimilarity(dim=1))
    char_history.append({'train_loss':train_loss.detach().cpu().numpy(), 'val_loss':val_loss.detach().cpu().numpy()})

epoch:  1 / 10
finished 40%
finished 80%
train loss = 0.389471
finished 40%
finished 80%
validation loss = 0.461490
epoch:  2 / 10
finished 40%
finished 80%
train loss = 0.323547
finished 40%
finished 80%
validation loss = 0.300803
epoch:  3 / 10
finished 40%
finished 80%
train loss = 0.282955
finished 40%
finished 80%
validation loss = 0.283042
epoch:  4 / 10
finished 40%
finished 80%
train loss = 0.275988
finished 40%
finished 80%
validation loss = 0.274604
epoch:  5 / 10
finished 40%
finished 80%
train loss = 0.270074
finished 40%
finished 80%
validation loss = 0.267032
epoch:  6 / 10
finished 40%
finished 80%
train loss = 0.267344
finished 40%
finished 80%
validation loss = 0.266217
epoch:  7 / 10
finished 40%
finished 80%
train loss = 0.267172
finished 40%
finished 80%
validation loss = 0.267049
epoch:  8 / 10
finished 40%
finished 80%
train loss = 0.266806
finished 40%
finished 80%
validation loss = 0.265845
epoch:  9 / 10
finished 40%
finished 80%
train loss = 0.266475
finished 40%
finished 80%
validation loss = 0.267974
epoch:  10 / 10
finished 40%
finished 80%
train loss = 0.266868
finished 40%
finished 80%
validation loss = 0.267379
CPU times: user 4min 33s, sys: 601 ms, total: 4min 34s
Wall time: 4min 34s
In [244]:
char_history
Out[244]:
[{'train_loss': array(0.3894706, dtype=float32),
  'val_loss': array(0.46149033, dtype=float32)},
 {'train_loss': array(0.32354674, dtype=float32),
  'val_loss': array(0.30080333, dtype=float32)},
 {'train_loss': array(0.2829547, dtype=float32),
  'val_loss': array(0.28304216, dtype=float32)},
 {'train_loss': array(0.2759877, dtype=float32),
  'val_loss': array(0.27460375, dtype=float32)},
 {'train_loss': array(0.2700736, dtype=float32),
  'val_loss': array(0.26703188, dtype=float32)},
 {'train_loss': array(0.26734442, dtype=float32),
  'val_loss': array(0.26621684, dtype=float32)},
 {'train_loss': array(0.2671719, dtype=float32),
  'val_loss': array(0.26704893, dtype=float32)},
 {'train_loss': array(0.26680568, dtype=float32),
  'val_loss': array(0.26584542, dtype=float32)},
 {'train_loss': array(0.26647538, dtype=float32),
  'val_loss': array(0.26797417, dtype=float32)},
 {'train_loss': array(0.2668677, dtype=float32),
  'val_loss': array(0.26737928, dtype=float32)}]
In [245]:
train_losses = [item['train_loss'] for item in char_history]
val_losses = [item['val_loss'] for item in char_history]
d = {"train_loss" : train_losses, "val_loss":val_losses}
plot_df = pd.DataFrame(data = d)
plot_df
Out[245]:
train_loss val_loss
0 0.3894706 0.46149033
1 0.32354674 0.30080333
2 0.2829547 0.28304216
3 0.2759877 0.27460375
4 0.2700736 0.26703188
5 0.26734442 0.26621684
6 0.2671719 0.26704893
7 0.26680568 0.26584542
8 0.26647538 0.26797417
9 0.2668677 0.26737928
In [246]:
plot_loss([i for i in range(epochs)],plot_df, "RMSE Character Level")

Test¶

In [247]:
char_ds_train = char_dataset(split_text(all_train_df),char_embedding, window_size)
test_char_ds = char_dataset(split_text(test_df),char_embedding, window_size)

train_loader = DataLoader(char_ds_train, batch_size=batch_size, shuffle = False)
test_loader = DataLoader(test_char_ds, batch_size=batch_size, shuffle = False)
In [248]:
char_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout )
In [249]:
%%time
epochs = 8
char_history = []
char_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, char_model, device, loss_fn, nn.CosineSimilarity(dim=1))

epoch:  1 / 8
finished 40%
finished 80%
train loss = 0.403725
epoch:  2 / 8
finished 40%
finished 80%
train loss = 0.315202
epoch:  3 / 8
finished 40%
finished 80%
train loss = 0.273885
epoch:  4 / 8
finished 40%
finished 80%
train loss = 0.270288
epoch:  5 / 8
finished 40%
finished 80%
train loss = 0.268247
epoch:  6 / 8
finished 40%
finished 80%
train loss = 0.266779
epoch:  7 / 8
finished 40%
finished 80%
train loss = 0.265701
epoch:  8 / 8
finished 40%
finished 80%
train loss = 0.265280
CPU times: user 3min 44s, sys: 589 ms, total: 3min 45s
Wall time: 3min 45s
In [250]:
%%time 
test_loss = test_loop(test_loader, char_model, device, loss_fn,  nn.CosineSimilarity(dim=1))

finished 40%
finished 80%
validation loss = 0.279276
CPU times: user 33.2 s, sys: 26.7 ms, total: 33.2 s
Wall time: 33.2 s

MAE¶

Training¶

In [340]:
char_ds_train = char_dataset(split_text(train_df),char_embedding, window_size)
char_ds_val = char_dataset(split_text(val_df),char_embedding, window_size)
char_ds_test = char_dataset(split_text(test_df),char_embedding, window_size)
In [341]:
train_loader = DataLoader(char_ds_train, batch_size=batch_size, shuffle = False)
val_loader = DataLoader(char_ds_val, batch_size=batch_size, shuffle = False)
In [342]:
loss_fn = nn.L1Loss()
char_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout )
In [343]:
%%time
epochs = 10
char_history = []
char_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, char_model, device, loss_fn, nn.CosineSimilarity(dim=1))
    val_loss = test_loop(val_loader, char_model, device, loss_fn,  nn.CosineSimilarity(dim=1))
    char_history.append({'train_loss':train_loss.detach().cpu().numpy(), 'val_loss':val_loss.detach().cpu().numpy()})

epoch:  1 / 10
finished 40%
finished 80%
train loss = 0.373941
finished 40%
finished 80%
validation loss = 0.461494
epoch:  2 / 10
finished 40%
finished 80%
train loss = 0.387875
finished 40%
finished 80%
validation loss = 0.461478
epoch:  3 / 10
finished 40%
finished 80%
train loss = 0.327150
finished 40%
finished 80%
validation loss = 0.323186
epoch:  4 / 10
finished 40%
finished 80%
train loss = 0.292096
finished 40%
finished 80%
validation loss = 0.460509
epoch:  5 / 10
finished 40%
finished 80%
train loss = 0.389313
finished 40%
finished 80%
validation loss = 0.461494
epoch:  6 / 10
finished 40%
finished 80%
train loss = 0.312647
finished 40%
finished 80%
validation loss = 0.290743
epoch:  7 / 10
finished 40%
finished 80%
train loss = 0.275292
finished 40%
finished 80%
validation loss = 0.271669
epoch:  8 / 10
finished 40%
finished 80%
train loss = 0.269167
finished 40%
finished 80%
validation loss = 0.265507
epoch:  9 / 10
finished 40%
finished 80%
train loss = 0.267074
finished 40%
finished 80%
validation loss = 0.265977
epoch:  10 / 10
finished 40%
finished 80%
train loss = 0.266229
finished 40%
finished 80%
validation loss = 0.265901
CPU times: user 4min 21s, sys: 588 ms, total: 4min 21s
Wall time: 4min 21s
In [344]:
train_losses = [item['train_loss'] for item in char_history]
val_losses = [item['val_loss'] for item in char_history]
d = {"train_loss" : train_losses, "val_loss":val_losses}
plot_df = pd.DataFrame(data = d)
plot_df
Out[344]:
train_loss val_loss
0 0.37394062 0.4614938
1 0.38787547 0.4614779
2 0.32715014 0.32318562
3 0.29209635 0.4605088
4 0.38931298 0.46149373
5 0.31264716 0.2907432
6 0.27529162 0.27166903
7 0.2691666 0.26550692
8 0.2670742 0.26597688
9 0.26622948 0.26590058
In [345]:
plot_loss([i for i in range(epochs)],plot_df, "MAE Character Level")

Test¶

In [346]:
char_ds_train = char_dataset(split_text(all_train_df),char_embedding, window_size)
test_char_ds = char_dataset(split_text(test_df),char_embedding, window_size)

train_loader = DataLoader(char_ds_train, batch_size=batch_size, shuffle = False)
test_loader = DataLoader(test_char_ds, batch_size=batch_size, shuffle = False)
In [347]:
char_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout )
In [348]:
%%time
epochs = 10
char_history = []
char_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, char_model, device, loss_fn, nn.CosineSimilarity(dim=1))

epoch:  1 / 10
finished 40%
finished 80%
train loss = 0.364603
epoch:  2 / 10
finished 40%
finished 80%
train loss = 0.275673
epoch:  3 / 10
finished 40%
finished 80%
train loss = 0.267366
epoch:  4 / 10
finished 40%
finished 80%
train loss = 0.265806
epoch:  5 / 10
finished 40%
finished 80%
train loss = 0.264917
epoch:  6 / 10
finished 40%
finished 80%
train loss = 0.264421
epoch:  7 / 10
finished 40%
finished 80%
train loss = 0.264248
epoch:  8 / 10
finished 40%
finished 80%
train loss = 0.264388
epoch:  9 / 10
finished 40%
finished 80%
train loss = 0.264145
epoch:  10 / 10
finished 40%
finished 80%
train loss = 0.264113
CPU times: user 4min 31s, sys: 791 ms, total: 4min 32s
Wall time: 4min 32s
In [ ]:
%%time 
test_loss = test_loop(test_loader, char_model, device, loss_fn,  nn.CosineSimilarity(dim=1))

finished 40%
finished 80%
validation loss = 0.276287
CPU times: user 33.6 s, sys: 45.8 ms, total: 33.7 s
Wall time: 33.7 s

Using classic ML model¶

In [ ]:
char_ds_train = char_dataset(split_text(all_train_df),char_embedding, window_size)
test_char_ds = char_dataset(split_text(test_df),char_embedding, window_size)
In [ ]:
In [ ]:
ml_char_train_loader = DataLoader(char_ds_train, batch_size=1, shuffle = False)
ml_char_test_loader = DataLoader(test_char_ds, batch_size = 1, shuffle = False)
In [352]:
X_char_train, y_char_train = gather_feature_extraction_samples(ml_char_train_loader, char_model)
X_char_test, y_char_test =  gather_feature_extraction_samples(ml_char_test_loader, char_model)

finished 20%
finished 40%
finished 60%
finished 80%
finished extracting
finished 20%
finished 40%
finished 60%
finished 80%
finished extracting
In [353]:
%%time
print("XGBoost character level fit time:")
xgb_regressor = xgb.XGBRegressor()
xgb_regressor.fit(X_char_train, y_char_train)

XGBoost character level fit time:
CPU times: user 58.4 s, sys: 63.6 ms, total: 58.4 s
Wall time: 9.87 s
Out[353]:
XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
             importance_type=None, interaction_constraints='',
             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, ...)
In [354]:
%%time
print("XGBoost character level predict time:")
xgb_pred = xgb_regressor.predict(X_char_test)

XGBoost character level predict time:
CPU times: user 392 ms, sys: 2.01 ms, total: 394 ms
Wall time: 68.7 ms
In [355]:
xgb_rmse = math.sqrt(mean_squared_error(y_char_test, xgb_pred))
xgb_mae = mean_absolute_error(y_char_test, xgb_pred)

print(f"Root Mean Squared Error: {xgb_rmse}")
print(f"Mean Absolute Error: {xgb_mae}")

Root Mean Squared Error: 0.5430872122434022
Mean Absolute Error: 0.44537633657455444
In [356]:
%%time
print("LightGBM character level fit time:")
lgb_regressor = lgb.LGBMRegressor()
lgb_regressor.fit(X_char_train, y_char_train)

LightGBM character level fit time:

/home/idoliv/.conda/envs/my_env/lib/python3.7/site-packages/sklearn/utils/validation.py:993: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().


CPU times: user 2.86 s, sys: 29 ms, total: 2.89 s
Wall time: 595 ms
Out[356]:
LGBMRegressor()
In [357]:
%%time
print("LightGBM character level predict time:")
lgb_pred = lgb_regressor.predict(X_char_test)

LightGBM character level predict time:
CPU times: user 444 ms, sys: 995 µs, total: 445 ms
Wall time: 78.1 ms
In [358]:
lgb_rmse = math.sqrt(mean_squared_error(y_char_test, lgb_pred))
lgb_mae = mean_absolute_error(y_char_test, lgb_pred)

print(f"Root Mean Squared Error: {lgb_rmse}")
print(f"Mean Absolute Error: {lgb_mae}")

Root Mean Squared Error: 0.5371770368216288
Mean Absolute Error: 0.44040891182318387

Word Level LSTM¶

configurations¶

In [ ]:
embedding_dim = 5
batch_size = 32
learning_rate = 1e-3
window_size = 90
lstm_hidden_dim = 15 
dropout = 0
num_layers = 1

pre process data¶

In [ ]:
word_train_df = split_text(train_df)
word_train_df.head()
Out[ ]:
id product_uid product_title search_term relevance product_title_length search_term_length
0 2 100001 [simpson, strong, tie, 12, gauge, angle] [metal, l, brackets] 3.00 33 13
1 3 100001 [simpson, strong, tie, 12, gauge, angle] [simpson, sku, able] 2.50 33 9
2 9 100002 [simpson, strong, tie, 12, gauge, angle] [simpson, strong, , ties] 3.00 79 9
3 16 100005 [simpson, strong, tie, 12, gauge, angle] [simpson, strong, tie, hcc668] 2.33 78 16
4 17 100005 [sterling, ensemble, 33, 1/4, in., x, 60, in.,... [bath, shower, kit] 2.67 78 18
In [ ]:
word_val_df = split_text(val_df)
word_val_df.head()
Out[ ]:
id product_uid product_title search_term relevance product_title_length search_term_length
0 178998 173850 [black, decker, 0.065, in., x, 40, ft., dual, ... [black, decke, weed, eaters] 2.67 71 15
1 179002 173854 [black, decker, 0.065, in., x, 40, ft., dual, ... [blakck, decker, edger] 2.33 65 19
2 179005 173855 [black, decker, 0.065, in., x, 40, ft., dual, ... [one, , , 0.065, spool] 2.33 67 25
3 179007 173856 [black, decker, 0.065, in., x, 40, ft., dual, ... [remington, weed, trimmer, spools] 1.33 46 23
4 179008 173856 [black, decker, 0.065, in., x, 40, ft., dual, ... [ryobi, dual, line, replacement] 1.33 46 17

after splitting the words, we will create embedding for the corpus

Embedding¶

In [ ]:
vector_size =3
corpus_splited = split_text(corpus) 
data_all = list(corpus_splited.product_title) + list(corpus_splited.search_term)
max_length = max([len(s) for s in data_all])
window_size = max_length
# data
word_embedding = Word2Vec(sentences=data_all, vector_size=embedding_dim, window=5, min_count=1)
word_embedding.train(data_all, total_examples=len(data_all), epochs =20)

WARNING:gensim.models.word2vec:Effective 'alpha' higher than previous training cycles
Out[ ]:
(49227278, 56612440)
In [ ]:
print(f'max sentence lentgh is {max_length}')

max sentence lentgh is 37
In [ ]:
word_embedding.wv['strong']
Out[ ]:
array([ -4.740241 ,   6.5030384, -11.877178 ,   1.6152806,  -3.30088  ],
      dtype=float32)

Dataset¶

In [187]:
def embed_word(sentence, embedding_model):
    return [embedding_model.wv[word] for word in sentence]

def pad_sentence(sentence, expected_len):
    if len(sentence) >  expected_len:
        return sentence[:expected_len]
    else:
        pad_len = expected_len -  len(sentence)
        return sentence + [0]*pad_len
In [188]:
class Word_dataset(Dataset):
    def __init__(self, data, embedding_model, window_size):
        self.data = data
        self.embedding_model = embedding_model
        self.window_size = window_size
        
    def __len__(self):
        return len(self.data.index)
    
    def __getitem__(self,idx):
        label = self.data.relevance[idx]
        product_title =  self.data.product_title[idx]
        product_title = pad_sentence(product_title,max_length)
        product_title_embedded = embed_word(product_title, self.embedding_model)
        search_term =  self.data.search_term[idx]
        search_term = pad_sentence(search_term,max_length)
        search_term_embedded = embed_word(search_term, self.embedding_model)
        
        return torch.FloatTensor(product_title_embedded) ,torch.FloatTensor(search_term_embedded), label
word_ds = Word_dataset(word_train_df,word_embedding, window_size)
word_ds_val =  Word_dataset(word_val_df,word_embedding, window_size)
In [189]:
train_loader = DataLoader(word_ds, batch_size=batch_size, shuffle = False)
val_loader = DataLoader(word_ds_val, batch_size=batch_size, shuffle = False)

RMSE¶

Training¶

In [190]:
loss_fn = nn.MSELoss()
word_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout)
In [191]:
%%time
epochs = 7
word_history = []
word_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, word_model, device, loss_fn ,nn.CosineSimilarity(dim=1))
    val_loss = test_loop(val_loader, word_model, device, loss_fn,  nn.CosineSimilarity(dim=1))
    word_history.append({'train_loss':train_loss.detach().cpu().numpy(), 'val_loss':val_loss.detach().cpu().numpy()})

epoch:  1 / 7
finished 40%
finished 80%
train loss = 0.298558
finished 40%
finished 80%
validation loss = 0.272779
epoch:  2 / 7
finished 40%
finished 80%
train loss = 0.271611
finished 40%
finished 80%
validation loss = 0.274137
epoch:  3 / 7
finished 40%
finished 80%
train loss = 0.269059
finished 40%
finished 80%
validation loss = 0.268505
epoch:  4 / 7
finished 40%
finished 80%
train loss = 0.267757
finished 40%
finished 80%
validation loss = 0.265192
epoch:  5 / 7
finished 40%
finished 80%
train loss = 0.266869
finished 40%
finished 80%
validation loss = 0.265060
epoch:  6 / 7
finished 40%
finished 80%
train loss = 0.266393
finished 40%
finished 80%
validation loss = 0.264817
epoch:  7 / 7
finished 40%
finished 80%
train loss = 0.265961
finished 40%
finished 80%
validation loss = 0.266546
CPU times: user 2min 33s, sys: 488 ms, total: 2min 34s
Wall time: 2min 33s
In [192]:
train_losses = [item['train_loss'] for item in word_history]
val_losses = [item['val_loss'] for item in word_history]
d = {"train_loss" : train_losses, "val_loss":val_losses}
plot_df = pd.DataFrame(data = d)
plot_df
Out[192]:
train_loss val_loss
0 0.2985583 0.27277932
1 0.27161077 0.27413702
2 0.26905888 0.26850545
3 0.26775676 0.2651921
4 0.26686928 0.26506007
5 0.26639268 0.26481706
6 0.26596126 0.2665456
In [193]:
plot_loss([i for i in range(epochs)],plot_df, "RMSE Word Level")

Test¶

In [194]:
word_ds_train = Word_dataset(split_text(all_train_df),word_embedding, window_size)
test_word_ds = Word_dataset(split_text(test_df),word_embedding, window_size)

train_loader = DataLoader(word_ds_train, batch_size=batch_size, shuffle = False)
test_loader = DataLoader(test_word_ds, batch_size=batch_size, shuffle = False)
In [195]:
word_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout )
In [196]:
%%time
epochs = 8
char_history = []
word_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, word_model, device, loss_fn, nn.CosineSimilarity(dim=1))

epoch:  1 / 8
finished 40%
finished 80%
train loss = 0.355315
epoch:  2 / 8
finished 40%
finished 80%
train loss = 0.270406
epoch:  3 / 8
finished 40%
finished 80%
train loss = 0.267324
epoch:  4 / 8
finished 40%
finished 80%
train loss = 0.266064
epoch:  5 / 8
finished 40%
finished 80%
train loss = 0.265828
epoch:  6 / 8
finished 40%
finished 80%
train loss = 0.265286
epoch:  7 / 8
finished 40%
finished 80%
train loss = 0.264331
epoch:  8 / 8
finished 40%
finished 80%
train loss = 0.264446
CPU times: user 3min, sys: 584 ms, total: 3min
Wall time: 3min
In [197]:
%%time 
test_loss = test_loop(test_loader, word_model, device, loss_fn,  nn.CosineSimilarity(dim=1))

finished 40%
finished 80%
validation loss = 0.279567
CPU times: user 27.6 s, sys: 19.7 ms, total: 27.6 s
Wall time: 27.6 s

MAE¶

Training¶

In [198]:
loss_fn = nn.L1Loss()
word_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout)
In [199]:
%%time
epochs = 7
word_history = []
word_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, word_model, device, loss_fn ,nn.CosineSimilarity(dim=1))
    val_loss = test_loop(val_loader, word_model, device, loss_fn,  nn.CosineSimilarity(dim=1))
    word_history.append({'train_loss':train_loss.detach().cpu().numpy(), 'val_loss':val_loss.detach().cpu().numpy()})

epoch:  1 / 7
finished 40%
finished 80%
train loss = 0.238498
finished 40%
finished 80%
validation loss = 0.220140
epoch:  2 / 7
finished 40%
finished 80%
train loss = 0.220567
finished 40%
finished 80%
validation loss = 0.212950
epoch:  3 / 7
finished 40%
finished 80%
train loss = 0.218954
finished 40%
finished 80%
validation loss = 0.211910
epoch:  4 / 7
finished 40%
finished 80%
train loss = 0.218442
finished 40%
finished 80%
validation loss = 0.211856
epoch:  5 / 7
finished 40%
finished 80%
train loss = 0.217947
finished 40%
finished 80%
validation loss = 0.209723
epoch:  6 / 7
finished 40%
finished 80%
train loss = 0.217351
finished 40%
finished 80%
validation loss = 0.209085
epoch:  7 / 7
finished 40%
finished 80%
train loss = 0.217677
finished 40%
finished 80%
validation loss = 0.211292
CPU times: user 3min 5s, sys: 544 ms, total: 3min 6s
Wall time: 3min 6s
In [200]:
train_losses = [item['train_loss'] for item in word_history]
val_losses = [item['val_loss'] for item in word_history]
d = {"train_loss" : train_losses, "val_loss":val_losses}
plot_df = pd.DataFrame(data = d)
plot_df
Out[200]:
train_loss val_loss
0 0.23849772 0.22013971
1 0.22056726 0.21295018
2 0.21895404 0.21191007
3 0.21844189 0.21185637
4 0.217947 0.20972332
5 0.21735103 0.20908546
6 0.21767707 0.2112921
In [208]:
plot_loss([i for i in range(epochs)],plot_df, "MAE Word Level")

Test¶

In [202]:
word_ds_train = Word_dataset(split_text(all_train_df),word_embedding, window_size)
test_word_ds = Word_dataset(split_text(test_df),word_embedding, window_size)

train_loader = DataLoader(word_ds_train, batch_size=batch_size, shuffle = False)
test_loader = DataLoader(test_word_ds, batch_size=batch_size, shuffle = False)
In [203]:
word_model = SiameseNetworkLSTM(embedding_dim, lstm_hidden_dim, num_layers = num_layers , dropout = dropout )
In [204]:
%%time
epochs = 8
char_history = []
word_model.to(device)
for epoch in range(epochs):
    print("epoch: ", epoch+1, "/", epochs)
    train_loss = train_loop(train_loader, word_model, device, loss_fn, nn.CosineSimilarity(dim=1))

epoch:  1 / 8
finished 40%
finished 80%
train loss = 0.242219
epoch:  2 / 8
finished 40%
finished 80%
train loss = 0.254456
epoch:  3 / 8
finished 40%
finished 80%
train loss = 0.251377
epoch:  4 / 8
finished 40%
finished 80%
train loss = 0.228102
epoch:  5 / 8
finished 40%
finished 80%
train loss = 0.229749
epoch:  6 / 8
finished 40%
finished 80%
train loss = 0.229511
epoch:  7 / 8
finished 40%
finished 80%
train loss = 0.230481
epoch:  8 / 8
finished 40%
finished 80%
train loss = 0.266103
CPU times: user 2min 59s, sys: 515 ms, total: 3min
Wall time: 3min

Classic Model transfer learning¶

In [209]:
train_word_ds =  Word_dataset(split_text(all_train_df),word_embedding, max_length)
test_word_ds =  Word_dataset(split_text(test_df),word_embedding, max_length)
In [210]:
ml_word_train_loader = DataLoader(train_word_ds, batch_size=1, shuffle = False)
ml_word_test_loader = DataLoader(test_word_ds, batch_size=1, shuffle = False)
In [211]:
X_word_train, y_word_train = gather_feature_extraction_samples(ml_word_train_loader,word_model)
X_word_test, y_word_test =  gather_feature_extraction_samples(ml_word_test_loader,word_model)

finished 20%
finished 40%
finished 60%
finished 80%
finished extracting
finished 20%
finished 40%
finished 60%
finished 80%
finished extracting
In [212]:
%%time
print("XGBoost word level fit time:")
xgb_regressor = xgb.XGBRegressor()
xgb_regressor.fit(X_word_train, y_word_train)

XGBoost word level fit time:
CPU times: user 15.6 s, sys: 37.9 ms, total: 15.6 s
Wall time: 2.67 s
Out[212]:
XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
             importance_type=None, interaction_constraints='',
             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, ...)
In [213]:
%%time
print("XGBoost word level predict time:")
xgb_pred = xgb_regressor.predict(X_word_test)

XGBoost word level predict time:
CPU times: user 367 ms, sys: 23 µs, total: 367 ms
Wall time: 67.1 ms
In [214]:
xgb_rmse = math.sqrt(mean_squared_error(y_word_test, xgb_pred))
xgb_mae = mean_absolute_error(y_word_test, xgb_pred)

print(f"Root Mean Squared Error: {xgb_rmse}")
print(f"Mean Absolute Error: {xgb_mae}")

Root Mean Squared Error: 0.5399519727683411
Mean Absolute Error: 0.44233238697052
In [215]:
%%time
print("LightGBM word level fit time:")
lgb_regressor = lgb.LGBMRegressor()
xgb_regressor.fit(X_word_train, y_word_train)
lgb_regressor.fit(X_word_train, y_word_train)

LightGBM word level fit time:

/home/idoliv/.conda/envs/my_env/lib/python3.7/site-packages/sklearn/utils/validation.py:993: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().


CPU times: user 17.8 s, sys: 55.9 ms, total: 17.8 s
Wall time: 3.18 s
Out[215]:
LGBMRegressor()
In [216]:
%%time
print("LightGBM word level predict time:")
lgb_pred = lgb_regressor.predict(X_word_test)

LightGBM word level predict time:
CPU times: user 571 ms, sys: 1.01 ms, total: 572 ms
Wall time: 111 ms
In [217]:
lgb_rmse = math.sqrt(mean_squared_error(y_word_test, lgb_pred))
lgb_mae = mean_absolute_error(y_word_test, lgb_pred)

print(f"Root Mean Squared Error: {lgb_rmse}")
print(f"Mean Absolute Error: {lgb_mae}")

Root Mean Squared Error: 0.5367348335124624
Mean Absolute Error: 0.43995475411689877